{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of use of Julia with the libraries JuMP, Ipopt and Spectra: peak fitting Infrared diffusion profil\n",
    "Charles Le Losq, 2016, Research School of Earth Sciences\n",
    "charles.lelosq@anu.edu.au\n",
    "\n",
    "We measured IR spectra following a 1D line along one principal axis of an olivine crystal, starting outside of the crystal and going inside toward its core. We want to determine the diffusion coefficient of hydrogen along this line. For doing so, we want to fit Gaussian peaks to the bands assigned to O-H stretching in olivine.\n",
    "\n",
    "We face a simple model where the amplitudes of the Gaussian peaks can be determined from the equation describing the diffusion of a molecule in 1D.\n",
    "\n",
    "Two files are taken as input: the data and a file containing the description of the data. See the comments in this file for understanding its use.\n",
    "    \n",
    "Typically, the user will need to modify the two lines after the call of the libraries, those lines describing where the files are.\n",
    "\n",
    "The third line can also be modified to get the plots and select a departure point (*e.g., what spectrum really is the first one in the olivine crystal).\n",
    "\n",
    "WARNING: A scaling coefficient of 10000 is applied to the data to bring the itnensities close to 1, in order to promote global convergence of the optimisation algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31802c310>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using PyPlot\n",
    "using JuMP\n",
    "using Ipopt\n",
    "using Spectra\n",
    "using StatsBase\n",
    "using JLD\n",
    "\n",
    "# you need to modify the following three lines\n",
    "data = readdlm(\"../Fo_exp6/Fo_exp6_P5.txt\", '\\t') #reading the data\n",
    "parameters = readdlm(\"../Fo_exp6/Fo_exp6_P5_params.txt\", '\\t') #reading the parametersthe experience details\n",
    "plot(data[find(2800 .< data[:,1] .<3900),1],data[find(2800 .< data[:,1] .<3900),25:35]) # to select the point of departure of the profil more easily\n",
    "\n",
    "# The following lines are internal cooking stuffs to get the parameters\n",
    "step = Float64(parameters[1,2]) #step is inputed in metre\n",
    "time = Float64(parameters[2,2].*3600.0) #time is in seconds, convert the hours of the user input\n",
    "start_sp = Int64(parameters[3,2]) #first valid spectra along profil (at the border)\n",
    "stop_sp = Int64(parameters[4,2]) #last valid spectra along profil (at the centre)\n",
    "low_x = Float64(parameters[5,2]) #low frequency of roi (region of interest)\n",
    "high_x = Float64(parameters[6,2]) #high frequency of roi (region of interest)\n",
    "first_bound_norm = Float64(parameters[7,2]) #low bond of integration for normalisation\n",
    "second_bound_norm = Float64(parameters[8,2]) #high bond of integration for normalisation\n",
    "first_bound_err = Float64(parameters[9,2]) #low bond of integration for error estimation\n",
    "last_bound_err = Float64(parameters[10,2]) #high bond of integration for error estimation\n",
    "m = Int64(parameters[11,2]) #number_peaks\n",
    "# We read the array of the model parameters and suqeeze them in 1D to have vectors\n",
    "c0_guess = squeeze(readdlm(IOBuffer(parameters[12,2]),','),1) #Initial concentrations\n",
    "c1_guess = squeeze(readdlm(IOBuffer(parameters[13,2]),','),1) #Bondary concentrations\n",
    "D_guess = squeeze(readdlm(IOBuffer(parameters[14,2]),','),1) #Diffisivity coefficients\n",
    "freq_guess = squeeze(readdlm(IOBuffer(parameters[15,2]),','),1) #Frequencies of the peaks\n",
    "hwhm_guess = squeeze(readdlm(IOBuffer(parameters[16,2]),','),1) #HWHM of the peaks\n",
    "\n",
    "println(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "# We prepare the data for the fit, for that we call the function IRdataprep\n",
    "x, y, y_ese_r, x_fit, y_fit, ese_fit = IRdataprep(data,step,start_sp,stop_sp,low_x,high_x,first_bound_norm,second_bound_norm, first_bound_err, last_bound_err)\n",
    "\n",
    "# Here we apply the 10000 scaling coefficient\n",
    "y = y .*10000\n",
    "y_fit = y_fit .*10000\n",
    "ese_fit = ese_fit .* 10000\n",
    "\n",
    "println(\"Done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model construction\n",
    "The following lines allow to build the model.\n",
    "\n",
    "For doing so we use JuMP (https://jump.readthedocs.org/en/latest/ and https://github.com/JuliaOpt/JuMP.jl). This allows to define our problem easily, without even being tied to a particular solver.\n",
    "\n",
    "In the following, we are going to use the Ipopt open-source solver (https://projects.coin-or.org/Ipopt). This can be changed regarding the preference of the user without affecting the model construction.\n",
    "\n",
    "This is the strenght of JuMP: being able to write clear models and then, play with different solvers / optins without re-writing everything.\n",
    "\n",
    "Constrains are also clearly indicated with JuMP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constructed...\n"
     ]
    }
   ],
   "source": [
    "# number of data =\n",
    "n = size(x_fit)[1]\n",
    "\n",
    "mod = Model(solver=IpoptSolver()) # we build the model with saying that we are going to use Ipopt\n",
    "\n",
    "#Initial values, m is the number of peaks\n",
    "@defVar(mod,g_c0[i=1:m] >= 0) # here we ask the concentrations to be positive while declaring them\n",
    "@defVar(mod,g_c1[i=1:m] >= 0)\n",
    "@defVar(mod, g_D[i=1:m] )\n",
    "@defVar(mod, g_freq[i=1:m] )\n",
    "@defVar(mod, g_hwhm[i=1:m] )\n",
    "\n",
    "# setting the initial values\n",
    "setValue(g_c0[i=1:m],c0_guess[i])\n",
    "setValue(g_c1[i=1:m],c1_guess[i])\n",
    "setValue(g_D[i=1:m],D_guess[i])\n",
    "setValue(g_freq[i=1:m],freq_guess[i])\n",
    "setValue(g_hwhm[i=1:m],hwhm_guess[i])\n",
    "\n",
    "# The generic expression to build the peaks: amplitude of a gaussian peak is calculated from the diffusion of a chemical specie along a diffusion profil\n",
    "@defNLExpr(mod,g_mod[j=1:n],sum{((g_c1[i] - g_c0[i]) * erfc(x_fit[j,1]/(2. * ( 10^g_D[i] * time)^0.5)) + g_c0[i]) *exp(-log(2) * (x_fit[j,2]-g_freq[i])^2/(g_hwhm[i]^2)), i=1:m})\n",
    "\n",
    "# The objective function to solve\n",
    "@setNLObjective(mod,Min,sum{(g_mod[j] - y_fit[j])^2, j=1:n})\n",
    "println(\"Constructed...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now plot the data to see how looks like the initial model. If not good, we update the above values. Change the variable \"selected_spectrum\" to see the results for different spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c0 are [0.1,0.1,0.1,0.1,0.1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x317e96d90>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameter extractions\n",
    "mod_co = getValue(g_c0)\n",
    "mod_c1 = getValue(g_c1)\n",
    "#mod_co = mod_co[:]*(y_max-y_min)+y_min\n",
    "#mod_c1 = mod_c1[:]*(y_max-y_min)+y_min\n",
    "mod_D = getValue(g_D)\n",
    "mod_f = getValue(g_freq)\n",
    "mod_hwhm = getValue(g_hwhm)\n",
    "\n",
    "selected_spectrum = 10 #for peak fitting figures\n",
    "model,peaks = peak_diffusion(mod_co, mod_c1, mod_D, mod_f, mod_hwhm, x_fit, time)\n",
    "interest_compos = peaks[(selected_spectrum-1)*length(x)+1:(selected_spectrum-1)*length(x)+length(x),:]\n",
    "\n",
    "fig = figure()\n",
    "#plot(x, y[:,:],color=\"black\",label=\"All profil\")\n",
    "plot(x, y[:,selected_spectrum],color=\"blue\",label=\"SOI\")\n",
    "plot(x,interest_compos[:,:],color=\"red\",label=\"Guess fit\")\n",
    "title(\"Spectrum $(selected_spectrum) plus its initial guess fit\")\n",
    "xlabel(L\"Wavenumber, cm$^{-1}$\")\n",
    "ylabel(\"Intensity, normalised\")\n",
    "\n",
    "println(\"c0 are $(mod_co)\")\n",
    "println(\"c1 are $(mod_c1)\")\n",
    "println(\"D are $(mod_D)\")\n",
    "println(\"frequency are $(mod_f)\")\n",
    "println(\"hwhm are $(mod_hwhm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we launch the optimisation procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c1 are [1.0,1.0,1.0,1.0,1.0]\n",
      "D are [-13.0,-13.0,-13.0,-13.0,-13.0]\n",
      "frequency are [3566.0,3578.0,3590.0,3613.0,3624.0]\n",
      "hwhm are [2.0,2.0,2.0,2.0,2.0]\n",
      "Solving...\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit http://projects.coin-or.org/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.12.4, running with linear solver mumps.\n",
      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:        0\n",
      "Number of nonzeros in inequality constraint Jacobian.:        0\n",
      "Number of nonzeros in Lagrangian Hessian.............:      325\n",
      "\n",
      "Total number of variables............................:       25\n",
      "                     variables with only lower bounds:       10\n",
      "                variables with lower and upper bounds:        0\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:        0\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  2.5875499e+02 0.00e+00 1.01e+02  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  1.3243009e+02 0.00e+00 4.41e+01  -1.0 4.99e-01   2.0 4.68e-01 1.00e+00f  1\n",
      "   2  5.9563755e+01 0.00e+00 2.23e+01  -1.0 6.38e-01   1.5 8.03e-01 1.00e+00f  1\n",
      "   3  2.7154809e+01 0.00e+00 9.30e+00  -1.0 6.20e-01   1.0 1.00e+00 1.00e+00f  1\n",
      "   4  1.8692594e+01 0.00e+00 2.40e+00  -1.0 3.89e-01   0.6 1.00e+00 1.00e+00f  1\n",
      "   5  1.6240027e+01 0.00e+00 4.65e-01  -1.0 3.65e-01   0.1 1.00e+00 1.00e+00f  1\n",
      "   6  1.5551499e+01 0.00e+00 2.21e+01  -1.7 5.52e+00    -  6.55e-01 5.00e-01f  2\n",
      "   7  1.4720617e+01 0.00e+00 6.49e-01  -1.7 2.69e-01  -0.4 1.00e+00 1.00e+00f  1\n",
      "   8  1.4711824e+01 0.00e+00 1.05e+00  -1.7 9.76e-01    -  1.00e+00 1.00e+00f  1\n",
      "   9  1.4597179e+01 0.00e+00 1.81e-01  -1.7 3.88e-01    -  1.00e+00 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  1.4590179e+01 0.00e+00 2.00e-02  -2.5 1.17e-01    -  1.00e+00 1.00e+00f  1\n",
      "  11  1.4590089e+01 0.00e+00 1.91e-04  -3.8 1.14e-02    -  1.00e+00 1.00e+00f  1\n",
      "  12  1.4590089e+01 0.00e+00 2.76e-07  -5.7 2.40e-04    -  1.00e+00 1.00e+00f  1\n",
      "  13  1.4590089e+01 0.00e+00 2.60e-11  -8.6 1.81e-06    -  1.00e+00 1.00e+00f  1\n",
      "\n",
      "Number of Iterations....: 13\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   7.6515470702172248e+00    1.4590089295490799e+01\n",
      "Dual infeasibility......:   2.6001836023140236e-11    4.9580706482350017e-11\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.5157774653462507e-09    4.7971237097732775e-09\n",
      "Overall NLP error.......:   2.5157774653462507e-09    4.7971237097732775e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 19\n",
      "Number of objective gradient evaluations             = 14\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 0\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 0\n",
      "Number of Lagrangian Hessian evaluations             = 13\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.076\n",
      "Total CPU secs in NLP function evaluations           =     11.682\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "Solver status: Optimal"
     ]
    }
   ],
   "source": [
    "# Solve for the control and state\n",
    "println(\"Solving...\")\n",
    "status = solve(mod)\n",
    "\n",
    "# Display results\n",
    "println(\"Solver status: \", status)\n",
    "println(\"rmsd: \", getObjectiveValue(mod))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a good solution. Let's see now how things look like (change the variable \"selected_spectrum\" to see the results for a different spectrum):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x318018190>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.text.Text object at 0x317dbd6d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameter extractions\n",
    "mod_co_f = getValue(g_c0)\n",
    "mod_c1_f = getValue(g_c1)\n",
    "mod_D_f = getValue(g_D)\n",
    "mod_f_f = getValue(g_freq)\n",
    "mod_hwhm_f = getValue(g_hwhm)\n",
    "\n",
    "selected_spectrum = 1 #for peak fitting figures\n",
    "model,peaks = peak_diffusion(mod_co_f, mod_c1_f, mod_D_f, mod_f_f, mod_hwhm_f, x_fit, time)\n",
    "interest_compos = peaks[selected_spectrum*length(x)+1:selected_spectrum*length(x)+length(x),:]\n",
    "\n",
    "fig = figure()\n",
    "#plot(x, y[:,:],color=\"black\",label=\"All profil\")\n",
    "plot(x, y[:,selected_spectrum],color=\"blue\",label=\"SOI\")\n",
    "plot(x,interest_compos[:,:],color=\"red\",label=\"Guess fit\")\n",
    "title(\"Spectrum $(selected_spectrum) plus its final fit\")\n",
    "xlabel(L\"Wavenumber, cm$^{-1}$\")\n",
    "ylabel(\"Intensity, normalised\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model parameters are\n",
      "c0 are [0.05611614105497245,0.025355638429358614,0.32337561780019153,0.0781045935228856,0.2689396811807845]\n",
      "c1 are [0.25602835759696424,1.081859776209491,0.6607206730678181,3.312780262206398,0.5405911219802675]\n",
      "D are [-13.622066851927913,-13.584198214190495,-13.8010504869554,-13.436727379122445,-13.716664084691317]\n",
      "frequency are [3568.4154477984303,3578.8973776012076,3590.6549656131183,3612.4926162451957,3624.1147259353047]\n",
      "hwhm are [3.614316722735779,3.0141171816048185,3.1409185247529035,2.4258582115845635,2.14351879066027]\n"
     ]
    }
   ],
   "source": [
    "println(\"The model parameters are\")\n",
    "println(\"c0 are $(mod_co_f)\")\n",
    "println(\"c1 are $(mod_c1_f)\")\n",
    "println(\"D are $(mod_D_f)\")\n",
    "println(\"frequency are $(mod_f_f)\")\n",
    "println(\"hwhm are $(mod_hwhm_f)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error estimation\n",
    "Error estimation is made with non-parametric bootstrapping: (i) we generate X new datasets; (ii) we fit those new dataset with the model and get X sets of parameters; (iii) we calculate the standard deviation of the parameters with the help of those X sets of parameters. This procedure is described in Efron and Tibshinari (1986, Statistical Science 1(1), 54-77)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c0 are [0.05611614105497245,0.025355638429358614,0.32337561780019153,0.0781045935228856,0.2689396811807845]\n",
      "c1 are [0.25602835759696424,1.081859776209491,0.6607206730678181,3.312780262206398,0.5405911219802675]\n",
      "D are [-13.622066851927913,-13.584198214190495,-13.8010504869554,-13.436727379122445,-13.716664084691317]\n",
      "frequency are [3568.4154477984303,3578.8973776012076,3590.6549656131183,3612.4926162451957,3624.1147259353047]\n",
      "hwhm are [3.614316722735779,3.0141171816048185,3.1409185247529035,2.4258582115845635,2.14351879066027]\n",
      "Iteration 1, solver status: Optimal\n",
      "Iteration 2, solver status: Optimal\n",
      "Iteration 3, solver status: Optimal\n",
      "Iteration 4, solver status: Optimal\n",
      "Iteration 5, solver status: Optimal\n",
      "Iteration 6, solver status: Optimal\n",
      "Iteration 7, solver status: Optimal\n",
      "Iteration 8, solver status: Optimal\n",
      "Iteration 9, solver status: Optimal\n",
      "Iteration 10, solver status: Optimal\n",
      "Iteration 11, solver status: Optimal\n",
      "Iteration 12, solver status: Optimal\n",
      "Iteration 13, solver status: Optimal\n",
      "Iteration 14, solver status: Optimal\n",
      "Iteration 15, solver status: Optimal\n",
      "Iteration 16, solver status: Optimal\n",
      "Iteration 17, solver status: Optimal\n",
      "Iteration 18, solver status: Optimal\n",
      "Iteration 19, solver status: Optimal\n",
      "Iteration 20, solver status: Optimal\n",
      "Iteration 21, solver status: Optimal\n",
      "Iteration 22, solver status: Optimal\n",
      "Iteration 23, solver status: Optimal\n",
      "Iteration 24, solver status: Optimal\n",
      "Iteration 25, solver status: Optimal\n",
      "Iteration 26, solver status: Optimal\n",
      "Iteration 27, solver status: Optimal\n",
      "Iteration 28, solver status: Optimal\n",
      "Iteration 29, solver status: Optimal\n",
      "Iteration 30, solver status: Optimal\n",
      "Iteration 31, solver status: Optimal\n",
      "Iteration 32, solver status: Optimal\n",
      "Iteration 33, solver status: Optimal\n",
      "Iteration 34, solver status: Optimal\n",
      "Iteration 35, solver status: Optimal\n",
      "Iteration 36, solver status: Optimal\n",
      "Iteration 37, solver status: Optimal\n",
      "Iteration 38, solver status: Optimal\n",
      "Iteration 39, solver status: Optimal\n",
      "Iteration 40, solver status: Optimal\n",
      "Iteration 41, solver status: Optimal\n",
      "Iteration 42, solver status: Optimal\n",
      "Iteration 43, solver status: Optimal\n",
      "Iteration 44, solver status: Optimal\n",
      "Iteration 45, solver status: Optimal\n",
      "Iteration 46, solver status: Optimal\n",
      "Iteration 47, solver status: Optimal\n",
      "Iteration 48, solver status: Optimal\n",
      "Iteration 49, solver status: Optimal\n",
      "Iteration 50, solver status: Optimal\n",
      "Iteration 51, solver status: Optimal\n",
      "Iteration 52, solver status: Optimal\n",
      "Iteration 53, solver status: Optimal\n",
      "Iteration 54, solver status: Optimal\n",
      "Iteration 55, solver status: Optimal\n",
      "Iteration 56, solver status: Optimal\n",
      "Iteration 57, solver status: Optimal\n",
      "Iteration 58, solver status: Optimal\n",
      "Iteration 59, solver status: Optimal\n",
      "Iteration 60, solver status: Optimal\n",
      "Iteration 61, solver status: Optimal\n",
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      "Done...\n"
     ]
    }
   ],
   "source": [
    "# test unique optimisqtion\n",
    "# bootstrapping data for errors\n",
    "nb_boot = 1000 # number of bootstrapping samples\n",
    "\n",
    "params_boot = Array{Float64}(nb_boot,5,m)\n",
    "vect = collect(1:size(x_fit)[1]) # for non-parametric bootstrapping\n",
    "\n",
    "c0_guess_b = c0_guess\n",
    "c1_guess_b = c1_guess\n",
    "D_guess_b = D_guess\n",
    "freq_guess_b = freq_guess\n",
    "hwhm_guess_b = hwhm_guess\n",
    "\n",
    "for k = 1:nb_boot\n",
    "    \n",
    "    # bootstrap by resampling y_fit with replacement...\n",
    "    idx = sample(vect,size(vect)[1])\n",
    "    b_y_f = y_fit[idx,:]\n",
    "    b_x_f = x_fit[idx,:]\n",
    "    \n",
    "    \n",
    "    modb = Model(solver=IpoptSolver(print_level=0)) # to be safe we re-define a model with no printing from Ipopt\n",
    "\n",
    "    @defVar(modb,g_c0_b[i=1:m] >= 0) #Initial values, m is the number of peaks\n",
    "    @defVar(modb,g_c1_b[i=1:m] >= 0)\n",
    "    @defVar(modb, g_D_b[i=1:m] )\n",
    "    @defVar(modb, g_freq_b[i=1:m] )\n",
    "    @defVar(modb, g_hwhm_b[i=1:m] >= 0 )\n",
    "\n",
    "    # setting the initial values\n",
    "    setValue(g_c0_b[i=1:m],c0_guess_b[i])\n",
    "    setValue(g_c1_b[i=1:m],c1_guess_b[i])\n",
    "    setValue(g_D_b[i=1:m],D_guess_b[i])\n",
    "    setValue(g_freq_b[i=1:m],freq_guess_b[i])\n",
    "    setValue(g_hwhm_b[i=1:m],hwhm_guess_b[i])\n",
    "    \n",
    "    # The expression and objective functions, followed by the solving query\n",
    "    @defNLExpr(modb,g_mod_b[j=1:n],sum{((g_c1_b[i] - g_c0_b[i]) * erfc(b_x_f[j,1]/(2. * ( 10^g_D_b[i] * time)^0.5)) + g_c0_b[i]) *exp(-log(2) * (b_x_f[j,2]-g_freq_b[i])^2/(g_hwhm_b[i]^2)), i=1:m})\n",
    "    @setNLObjective(modb,Min,sum{(g_mod_b[j] - b_y_f[j])^2,j=1:n})\n",
    "    status = solve(modb)\n",
    "    println(\"Iteration $(k), solver status: \", status) # we print some information\n",
    "    \n",
    "    # we record the bootstrapped parameters\n",
    "    params_boot[k,1,:] = getValue(g_c0_b)\n",
    "    params_boot[k,2,:] = getValue(g_c1_b)\n",
    "    params_boot[k,3,:] = getValue(g_D_b)\n",
    "    params_boot[k,4,:] = getValue(g_freq_b)\n",
    "    params_boot[k,5,:] = getValue(g_hwhm_b)\n",
    "end\n",
    "        \n",
    "\n",
    "println(\"Done...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following allows us to have a look at the bootstrapping results. If the bootstrapping is successful, we should see a plateau in the bootstrapping graphic. Change param_interest_boot_stat to see results for different parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From bootstrapping, values for peak 1:\n",
      "c0 mean [0.056169719124361805] and std [0.0013456995263988634]\n",
      "c1 mean [0.2570841903022188] and std [0.011966392749686874]\n",
      "D mean [-13.634396785607667] and std [0.26512645269843604]\n",
      "frequency mean [3568.4183219365923] and std [0.080547944952497]\n",
      "hwhm mean [3.609502872764248] and std [0.06544430688997407]\n",
      "\n",
      "From bootstrapping, values for peak 2:\n",
      "c0 mean [0.02531382340253194] and std [0.000731424935289026]\n",
      "c1 mean [1.0822905342784168] and std [0.012986989275213894]\n",
      "D mean [-13.584467842979118] and std [0.009785110083715584]\n",
      "frequency mean [3578.900274122754] and std [0.028977841717851508]\n",
      "hwhm mean [3.0133629593171243] and std [0.04823347793678726]\n",
      "\n",
      "From bootstrapping, values for peak 3:\n",
      "c0 mean [0.32335241742922094] and std [0.0019377462825305256]\n",
      "c1 mean [0.6611485058846222] and std [0.011095992487573392]\n",
      "D mean [-13.80190118106856] and std [0.03935024671848219]\n",
      "frequency mean [3590.655620775315] and std [0.01531692188192496]\n",
      "hwhm mean [3.141418164479674] and std [0.03056087243331631]\n",
      "\n",
      "From bootstrapping, values for peak 4:\n",
      "c0 mean [0.07806866165487372] and std [0.0014187016049113043]\n",
      "c1 mean [3.311853534581403] and std [0.023853667358663608]\n",
      "D mean [-13.436499752431393] and std [0.005386331255564654]\n",
      "frequency mean [3612.494020374672] and std [0.017013421822774186]\n",
      "hwhm mean [2.425761463164803] and std [0.019723421193527137]\n",
      "\n",
      "From bootstrapping, values for peak 5:\n",
      "c0 mean [0.26893035426229056] and std [0.0015102831562140988]\n",
      "c1 mean [0.5416800847405535] and std [0.01284728222824154]\n",
      "D mean [-13.724933362398177] and std [0.16289869546186525]\n",
      "frequency mean [3624.1152904930964] and std [0.01161128815719613]\n",
      "hwhm mean [2.144005093464093] and std [0.013351134255684731]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i = 1:m\n",
    "    println(\"From bootstrapping, values for peak $(i):\")\n",
    "    println(\"c0 mean $(mean(params_boot[:,1,i],1)) and std $(std(params_boot[:,1,i],1))\")\n",
    "    println(\"c1 mean $(mean(params_boot[:,2,i],1)) and std $(std(params_boot[:,2,i],1))\")\n",
    "    println(\"D mean $(mean(params_boot[:,3,i],1)) and std $(std(params_boot[:,3,i],1))\")\n",
    "    println(\"frequency mean $(mean(params_boot[:,4,i],1)) and std $(std(params_boot[:,4,i],1))\")\n",
    "    println(\"hwhm mean $(mean(params_boot[:,5,i],1)) and std $(std(params_boot[:,5,i],1))\")\n",
    "    println(\"\")\n",
    "end\n",
    "\n",
    "# bootstrap performance recorder\n",
    "bootrecord = zeros(nb_boot,5,m)\n",
    "for k = 1:nb_boot\n",
    "    for j = 1:5\n",
    "    for i = 1:m\n",
    "        if k == 0\n",
    "            bootrecord[k,j,i] = 0\n",
    "        else\n",
    "            bootrecord[k,j,i] = sum(std(params_boot[1:k,j,i],1))\n",
    "        end\n",
    "    end\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31f8a5810>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x32016e290>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "([1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0  …  4.0,1.0,2.0,3.0,3.0,2.0,1.0,3.0,1.0,2.0],[-13.6183,-13.6177,-13.6171,-13.6165,-13.6159,-13.6154,-13.6148,-13.6142,-13.6136,-13.613  …  -13.5651,-13.5645,-13.5639,-13.5634,-13.5628,-13.5622,-13.5616,-13.561,-13.5604,-13.5599],Any[PyObject <matplotlib.patches.Rectangle object at 0x320aaf210>,PyObject <matplotlib.patches.Rectangle object at 0x320aaf890>,PyObject <matplotlib.patches.Rectangle object at 0x320aaff10>,PyObject <matplotlib.patches.Rectangle object at 0x320abc5d0>,PyObject <matplotlib.patches.Rectangle object at 0x320abcc50>,PyObject <matplotlib.patches.Rectangle object at 0x320ac8310>,PyObject <matplotlib.patches.Rectangle object at 0x320ac8990>,PyObject <matplotlib.patches.Rectangle object at 0x320ac8e90>,PyObject <matplotlib.patches.Rectangle object at 0x320ad56d0>,PyObject <matplotlib.patches.Rectangle object at 0x320ad5d50>  …  PyObject <matplotlib.patches.Rectangle object at 0x320c89bd0>,PyObject <matplotlib.patches.Rectangle object at 0x320c95290>,PyObject <matplotlib.patches.Rectangle object at 0x320c95910>,PyObject <matplotlib.patches.Rectangle object at 0x320c95f90>,PyObject <matplotlib.patches.Rectangle object at 0x320ca2650>,PyObject <matplotlib.patches.Rectangle object at 0x320ca2cd0>,PyObject <matplotlib.patches.Rectangle object at 0x320cb1390>,PyObject <matplotlib.patches.Rectangle object at 0x320cb1a10>,PyObject <matplotlib.patches.Rectangle object at 0x31f7fac50>,PyObject <matplotlib.patches.Rectangle object at 0x320151f10>])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bootstrap performance\n",
    "param_interest_boot_stat = 3 # choose the parameter you want to look at\n",
    "peak_interest_boot_stat = 2 # for histogram, choose the parameter you want to look at\n",
    "\n",
    "# Graphics\n",
    "fig = figure()\n",
    "for i = 1:m\n",
    "    plot(1:nb_boot, bootrecord[:,param_interest_boot_stat,peak_interest_boot_stat],label=\"SOI\")\n",
    "end\n",
    "xlabel(\"Number of iterations during bootstrap\")\n",
    "ylabel(\"Error\")\n",
    "\n",
    "fi2= figure()\n",
    "h = plt[:hist](params_boot[:,param_interest_boot_stat,peak_interest_boot_stat],100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# TODO\n",
    "To be implemented soon: \n",
    "- a better way to get confidence interval from bootstrap would be to take the percentil (see... Wikipedia)\n",
    "Three different approaches for generating the new datasets can be adopted:\n",
    "- parametric bootstrrapping: resampling with randomly picking the new datapoints from the distribution of the existing datapoints (assumes a random distribution, with the standard error returned by the function IRdataprep; this is parametric bootstrapping);\n",
    "- smooth bootstrapping: resampling with replacement AND randomly affecting each new point by an error generated from the PDF described above OR following a standard deviation equal to 1/sqrt(n) with n = length(y_fit) (smooth bootstraping)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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